# Deep demosaicking for multispectral filter arrays

arXiv: https://arxiv.org/abs/1808.08021 · HTML: https://mlsanity.com/paper/1808.08021/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2018.

## Built on by (most cited first, 5 of 5)

- [Unsupervised Spectral Demosaicing with Lightweight Spectral Attention Networks](https://mlsanity.com/paper/2307.01990/) (2023)
- [MSFA-Frequency-Aware Transformer for Hyperspectral Images Demosaicing](https://mlsanity.com/paper/2303.13404/) (2023)
- [Real-time division-of-focal-plane polarization imaging system with progressive networks](https://mlsanity.com/paper/2110.13823/) (2021)
- [Efficient and Accurate Hyperspectral Image Demosaicing with Neural Network Architectures](https://mlsanity.com/paper/2403.12050/) (2023)
- [Generative Model-Assisted Demosaicing for Cross-multispectral Cameras](https://mlsanity.com/paper/2503.02322/) (2025)

## Abstract (arXiv)

We propose a novel demosaicking method for multispectral filter arrays based on a deep convolutional neural network. The proposed method first interpolates mosaicked multispectral images utilizing a bilinear approach, then applies a residual network to initial demosaicked images. The residual network consists of various three-dimensional convolutional layers and a rectified linear unit for describing the features of a multispectral data cube. Experimental results reveal that the proposed method outperforms conventional demosaicking methods.